Publishing data for analysis from a micro data table containing
sensitive attributes, while maintaining individual privacy, is a
problem of increasing significance today. The k-anonymity model
was proposed for privacy preserving data publication. While focusing
on identity disclosure, k-anonymity model fails to protect
attribute disclosure to some extent. Many efforts are made to
enhance the k-anonymity model recently. In this paper, we propose
two new privacy protection models called (p, α)-sensitive
k-anonymity and (p+, α)-sensitive k-anonymity,
respectively. Different from previous the p-sensitive
k-anonymity model, these new introduced models allow us to release
a lot more information without compromising privacy. Moreover, we
prove that the (p, α)-sensitive and (p+, α)-sensitive
k-anonymity problems are NP-hard. We also include testing and
heuristic generating algorithms to generate desired micro data
table. Experimental results show that our introduced model could
significantly reduce the privacy breach.